• Transactions of the Society of Information Storage Systems
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• v.1 no.2
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• pp.155-160
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• 2005

In this paper, the channel decoder promising reliable data retrieving in noisy holographic channel has been developed for holographic WORM(write once read many) system. It covers various DSP(digital signal processing) blocks, such as align mark detector, adaptive channel equalizer, modulation decoder and ECC(error correction code) decoder. The specific schemes of DSP are designed to reduce the effect of noises in holographic WORM(H-WORM) system, particularly in prototype of DAEWOO electronics(DEPROTO). For real time data retrieving, the channel decoder is redesigned for FPGA(field programmable gate array) based hardware, where DSP blocks calculate in parallel sense with memory buffers between blocks and controllers for driving peripherals of FPGA. As an input source of the experiments, MPEG2 TS(transport stream) data was used and recorded to DEPROTO system. During retrieving, the CCD(charge coupled device), capturing device of DEPROTO, detects retrieved images and transmits signals of them to the FPGA of hardware channel decoder. Finally, the output data stream of the channel decoder was transferred to the MPEG decoding board for monitoring video signals. The experimental results showed the error corrected BER(bit error rate) of less than $10^{-9}$, from the raw BER of DEPROTO, about $10^{-3}$. With the developed hardware channel decoder, the real-time video demonstration was possible during the experiments. The operating clock of the FPGA was 60 MHz, of which speed was capable of decoding up to 120 mega channel bits per sec.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.10
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• pp.483-488
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• 2016

In this paper, we propose a prototype-based classification learning by using the nearest-neighbor rule. The nearest-neighbor is applied to segment the class area of all the training data with hyperspheres, and a hypersphere must cover the data from the same class. The radius of a hypersphere is computed by the mid point of the two distances to the farthest same class point and the nearest other class point. And we transform the prototype selection problem into a set covering problem in order to determine the smallest set of prototypes that cover all the training data. The proposed prototype selection method is designed by a greedy algorithm and applicable to process a large-scale training set in parallel. The prediction rule is the nearest-neighbor rule and the new training data is the set of prototypes. In experiments, the generalization performance of the proposed method is superior to existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.22 no.10
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• pp.1277-1286
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• 2018

In this paper, we provide flexible scalability of computing resources in cloud environment and Apache Hadoop based cloud environment for analysis of public medical information big data. In fact, it includes the ability to quickly and flexibly extend storage, memory, and other resources in a situation where log data accumulates or grows over time. In addition, when real-time analysis of accumulated unstructured log data is required, the system adopts Hadoop-based analysis module to overcome the processing limit of existing analysis tools. Therefore, it provides a function to perform parallel distributed processing of a large amount of log data quickly and reliably. Perform frequency analysis and chi-square test for big data analysis. In addition, multivariate logistic regression analysis of significance level 0.05 and multivariate logistic regression analysis of meaningful variables (p<0.05) were performed. Multivariate logistic regression analysis was performed for each model 3.

• The KIPS Transactions:PartC
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• v.13C no.1 s.104
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• pp.83-94
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• 2006

This paper describes ATM cell encipherment method using Rijndael Algorithm adopted as an AES(Advanced Encryption Standard) by NIST in 2001. ISO 9160 describes the requirement of physical layer data processing in encryption/decryption. For the description of ATM cell encipherment method, we implemented ATM data encipherment equipment which satisfies the requirements of ISO 9160, and verified the encipherment/decipherment processing at ATM STM-1 rate(155.52Mbps). The DES algorithm can process data in the block size of 64 bits and its key length is 64 bits, but the Rijndael algorithm can process data in the block size of 128 bits and the key length of 128, 192, or 256 bits selectively. So it is more flexible in high bit rate data processing and stronger in encription strength than DES. For tile real time encryption of high bit rate data stream. Rijndael algorithm was implemented in FPGA in this experiment. The boundary of serial UNI cell was detected by the CRC method, and in the case of user data cell the payload of 48 octets (384 bits) is converted in parallel and transferred to 3 Rijndael encipherment module in the block size of 128 bits individually. After completion of encryption, the header stored in buffer is attached to the enciphered payload and retransmitted in the format of cell. At the receiving end, the boundary of ceil is detected by the CRC method and the payload type is decided. n the payload type is the user data cell, the payload of the cell is transferred to the 3-Rijndael decryption module in the block sire of 128 bits for decryption of data. And in the case of maintenance cell, the payload is extracted without decryption processing.

• Nuclear Medicine and Molecular Imaging
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• v.43 no.5
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• pp.459-467
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• 2009

Purpose: The maximum likelihood-expectation maximization (ML-EM) is the statistical reconstruction algorithm derived from probabilistic model of the emission and detection processes. Although the ML-EM has many advantages in accuracy and utility, the use of the ML-EM is limited due to the computational burden of iterating processing on a CPU (central processing unit). In this study, we developed a parallel computing technique on GPU (graphic processing unit) for ML-EM algorithm. Materials and Methods: Using Geforce 9800 GTX+ graphic card and CUDA (compute unified device architecture) the projection and backprojection in ML-EM algorithm were parallelized by NVIDIA's technology. The time delay on computations for projection, errors between measured and estimated data and backprojection in an iteration were measured. Total time included the latency in data transmission between RAM and GPU memory. Results: The total computation time of the CPU- and GPU-based ML-EM with 32 iterations were 3.83 and 0.26 see, respectively. In this case, the computing speed was improved about 15 times on GPU. When the number of iterations increased into 1024, the CPU- and GPU-based computing took totally 18 min and 8 see, respectively. The improvement was about 135 times and was caused by delay on CPU-based computing after certain iterations. On the other hand, the GPU-based computation provided very small variation on time delay per iteration due to use of shared memory. Conclusion: The GPU-based parallel computation for ML-EM improved significantly the computing speed and stability. The developed GPU-based ML-EM algorithm could be easily modified for some other imaging geometries.

• Proceedings of the KSR Conference
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• 2011.05a
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• pp.7-13
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• 2011

Simulation study were performed for ventilation capability effect on the smoke spread in the deeply-underground subway station(DUSS). Singeumho station(The line # 5, Depth: 46m) was modeled and were analyzed for smoke-spread speed difference between the originally-designed-ventilation-capacity and the measured-ventilation-capacity. Field test data for actual fan in DUSS was applied as a boundary condition of a simulation. The whole station was covered in this analysis and total of 4 million grids were generated for this simulation. The fire-driven flow was analyzed case by case to compare the smoke-spread effects. In order to enhance the efficiency of calculation, parallel processing by MPI was employed and large eddy simulation method in FDS code was adopted.

• Proceedings of the KSR Conference
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• 2011.10a
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• pp.2885-2890
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• 2011

Simulation study were performed for fire location effect on the smoke spread in the deeply-underground subway station(DUSS). In this research, Shingumho station (The line # 5, Depth: 46m) has been selected as case-study for the analysis of smoke-spread effect with the different fire location. Field test data measured for actual fan in DUSS was applied as a condition of a simulation. The whole station was covered in this analysis and 4 million grids were generated for this simulation. The fire driven flow was analyzed case by case to compare the smoke-spread effect according to the fire location. In order to enhance the efficiency of calculation, parallel processing by MPI was employed and LES(large eddy simulation) method in FDS code was adopted.

• The Journal of the Acoustical Society of Korea
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• v.18 no.1
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• pp.3-9
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• 1999

This paper concerns performance of a parallel digital signal processing system. The performance of the system is analyzed in terms of CPU cycles required for 1024-point FFT computation. The number of cycles is estimated in three different approaches; FFT algorithm-based, assembly level source code-based, and probability-based. The results of analysis indicate that on a bus-based system the best performance for FFT is achieved with a single board. Because in some applications like FFT, where frequent data exchanges among processors occur, the number of communication cycles increases as the number of boards. It is observed that inter-board communication degrades overall system performance for the FFT computation. Also shown is that linear increase in performance can be obtained if multiple buses are employed.

• Journal of Korea Multimedia Society
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• v.23 no.1
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• pp.31-42
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• 2020

During the 4th Industrial Revolution, service platforms utilizing diverse contents are emerging, and research on recommended systems that can be customized to users to provide quality service is being conducted. hybrid recommendation systems that provide high accuracy recommendations are being researched in various domains, and various filtering techniques, machine learning, and deep learning are being applied to recommended systems. However, in a recommended service environment where data must be analyzed and processed real time, the accuracy of the recommendation is important, but the computational speed is also very important. Due to high level of model complexity, a hybrid recommendation system or a Deep Learning-based recommendation system takes a long time to calculate. In this paper, a Cascade-hybrid recommended algorithm is proposed that can reduce the computational time while maintaining the accuracy of the recommendation. The proposed algorithm was designed to reduce the complexity of the model and minimize the computational speed while processing sequentially, rather than using existing weights or using a hybrid recommendation technique handled in parallel. Therefore, through the algorithms in this paper, contents can be analyzed and recommended effectively and real time through services such as SNS environments or shared economy platforms.

• Korean Journal of Cognitive Science
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• v.6 no.2
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• pp.5-27
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• 1995

Based on the given data if three experiments that measured word-by-word reading times of the Korean relative-clause sentences,parsing strategies and performance structures in comprehending Korean sentences were suggested.First,results of the significantily longer reading time of nouns than verbs suggested that Korean parsing processing would be primarily occurred at nouns.Seond,four parsing strategies were proposed to explain increased reading times,working memory loads,and parallel function effects.Third,performance structures of sentence comprehension were constructed from the interword reading time differences.The proposed strategies and structures seem to account for the patterns of word-by-word reading times of the five types of the Korean relative-clause se various ideas for further experimentation were discussed.